agentmods.dev

data-context-extractor

A skill for capturing a company's unwritten knowledge about its data warehouse—the storage system where business data is kept—and turning it into project-specific guidance.

In plain words
What is it for?
Use it to document tables and columns, define business metrics, record common queries and data caveats, describe relationships and update timing, and generate a reusable data-analysis skill.
Why use it?
It reduces repeated questions and misunderstandings about table meanings, metrics, joins, freshness, and internal terminology. It can use a schema, diagram, or team interview as its starting point.

Skill for Claude CodeCodex

5 2d ago A scan 91 tokens original Apache-2.0
View the source file NOMARJ/sigil
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/nomarj/sigil/data-context-extractor
Any agent
npx skills add NOMARJ/sigil --skill data-context-extractor
Clone the repo
git clone --depth 1 https://github.com/NOMARJ/sigil

Made for: Claude Code, Codex.

Per session 91 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 684 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00091 $0.00684
Opus 5 $0.00046 $0.00342
Sonnet 5 $0.00018 $0.00137
Haiku 4.5 $0.00009 $0.00068

Measured 2d ago against content hash 891bececb9b0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-context-extractor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

packs/data/skills/data-engineering/data-context-extractor/SKILL.md · 92 lines

How it starts

The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Context Extractor

Build a project-specific data skill that teaches Claude your company's data warehouse, terminology, metrics definitions, and common query patterns. Two modes:

Bootstrap Mode

Trigger: No existing data context skill for this project.

Process

  1. Schema Discovery

    • If database access available: query information_schema or equivalent
    • If not: ask user to paste/upload schema, ERD, or table descriptions
    • Discover: tables, columns, types, relationships, row counts

  2. Tribal Knowledge Interview (ask these questions):

    • What are the 5 most common queries your analysts run?
    • What metrics does the business track? How are they calculated?
    • What's the grain of each major table? (one row = one what?)
    • Are there any gotchas? (deleted rows not actually deleted, timezone mismatches, denormalized fields)
    • What terminology does the team use that wouldn't be obvious? (internal names, abbreviations)
    • What's the data freshness? (real-time, hourly, daily, weekly)
    • Which tables join to which? What are the common join patterns?

  3. Generate the Skill Write a data-context/SKILL.md in the project's .claude/skills/ (or .nomark/data/) containing:

    • Schema summary (tables, key columns, grain, relationships)
    • Metrics dictionary (metric name → SQL definition)
    • Common query patterns (templated SQL for frequent questions)
    • Gotchas and caveats
    • Terminology glossary
    • Data freshness expectations

Iteration Mode

Trigger: Existing data context skill needs updating.

Process

  1. Load existing skill
  2. Ask targeted questions about the gap (new table, new metric, changed business logic)
  3. Update the relevant section
  4. Note the change in an amendment log

Output Format

# Data Context — [Project Name]

## Schema Overview
| Table | Grain | Key Columns | Freshness | Notes |
|-------|-------|-------------|-----------|-------|

## Metrics Dictionary
| Metric | Definition (SQL) | Business Context |
|--------|-----------------|-----------------|

## Common Query Patterns
### [Question type]
```sql
-- Template query

Gotchas

  • [Gotcha 1]
  • [Gotcha 2]

Terminology

Term Meaning

Amendment Log

Date Change Author

Read the full file on GitHub · 92 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 92 lines · 91 tokens per session scan A 891bececb9b0

Subscribe to this mod's changes

data-context-extractor is a skill published in the GitHub repository NOMARJ/sigil (5 stars, last pushed 2d ago), licensed Apache-2.0. It adds 91 tokens to every session and 684 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metricsDatabasesSecurityai-agentscli-toolcode-scanningdependency-scanningdevtoolsmalware-detection
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